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مدلسازی و شبیه‌سازی بیوسنسور آنزیمی برای تشخیص آفلاتوکسین B1 با استفاده از شبکه عصبی مصنوعی | ||
| مهندسی بیوسیستم ایران | ||
| مقاله 3، دوره 51، شماره 1، فروردین 1399، صفحه 23-35 اصل مقاله (1.5 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijbse.2019.290736.665232 | ||
| نویسندگان | ||
| سید جواد سجادی؛ سلیمان حسین پور* ؛ شاهین رفیعی | ||
| گروه مهندسی مکانیک ماشین‌های کشاورزی، دانشکده مهندسی و فناوری پردیس کشاورزی و منابع طبیعی دانشگاه تهران | ||
| چکیده | ||
| افلاتوکسین B1 (AFB1) سمی ترین گروه آفلاتوکسین‌هاست که باعث آلودگی محصولات کشاورزی شده و اثرات مرگ باری بر سلامت انسان دارد. تشخیص AFB1 در مواد غذایی و خوراکی توسط بیوسنسورها سریع، کم هزینه و دقیق است. در این مقاله به مدلسازی و شبیه‌سازی واکنش‌های شیمیایی در بیوسنسور پتانسیومتری AFB1 جهت تعیین ثابت‌های بهینه نرخ واکنش پرداخته شده است. شبیه‌سازی واکنش‌های شیمیایی توسط نرم افزار COMSOL و بهینه سازی ثابت‌هاینرخ واکنش توسط شبکه عصبی مصنوعی و الگوریتم ژنتیک انجام شد. علاوه بر آن شبکه عصبی مصنوعی به عنوان تابع هدف مورد استفاده در الگوریتم ژنتیک به کار رفت. داده های تولید شده در مرحله شبیه سازی جهت آموزش و ارزیابی عملکرد شبکه عصبی مورد استفاده قرار گرفتند. نتایج بدست آمده نشان داد مدل COMSOL در مقایسه با داده‌های تجربی، پاسخ بیوسنسور را با MAPE برابر با 1023/0 % شبیه سازی کرد. شبکه عصبی مصنوعی آموزش داده شده با ساختار 1-48-5 نیز قادر به پیش بینی پاسخ بیوسنسور با MAPE برابر با 7074/0 % ، 9458/0 % ، 7473/0 % و 7492/0 % به ترتیب برای داده‌های گروه آموزش، اعتبار سنجی، آزمون و کل داده‌ها بود. نتایج بهینه‌سازی ثابت‌های نرخ واکنش توسط الگوریتم ژنتیک و شبکه عصبی مصنوعی نشان داد شبیه سازی پاسخ بیوسنسور AFB1 با استفاده از شبکه عصبی مصنوعی و پارامتر‌های ورودی انتخاب شده توسط الگوریتم ژنتیک دارای کمترین خطای MAPE برابر با 0026/0 % در پیش بینی میزان مهار آنزیم AChE توسط AFB1 است. | ||
| کلیدواژه‌ها | ||
| بیوسنسور؛ مدلسازی؛ کامسول؛ شبکه عصبی مصنوعی؛ الگوریتم ژنتیک | ||
| عنوان مقاله [English] | ||
| Modeling and Simulation of Enzymatic Biosensor for Detecting Aflatoxin B1 Using Artificial Neural Network | ||
| نویسندگان [English] | ||
| Sayed Javad Sajadi؛ Soleiman Hosseinpour؛ shahin rafiee | ||
| Agricultural Machinery Engineering Dept., Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran. | ||
| چکیده [English] | ||
| Aflatoxin B1 (AFB1) is one of the most toxic Aflatoxins that contaminates agricultural products and causes deathlike effects on human health. Determination of AFB1 in food by biosensors is fast, low cost and accurate. In this paper, modeling and simulation of chemical reactions in the AFB1 potentiometric biosensor is performed to determine the optimal reaction rate constants. Enzymatic reactions are simulated using COMSOL software and reaction rates are optimized by Artificial Neural Network (ANN) and Genetic Algorithm (GA). The fitness function of GA is defined by deploying ANN. The data generated during the simulation step were used to train and evaluate the performance of the neural network. Compared with experimental data, COMSOL model simulated biosensor response with MAPE equal to 0.1023 %. In addition trained ANN with 5-48-1 structure predicted biosensor response with MAPEs equal to 0.7074 %, 0.9458 %, 0.7473 % and 0.7492 % for train, validation, test and total data sets respectively. Reaction rates were optimized by Artificial Neural Network (ANN) and Genetic Algorithm. Modeling results showed that trained Neural Network using Genetic Algorithm optimized reaction rates has the lowest MAPE equal to 0.0026 % compared with other models in prediction of AChE enzyme inhibition by AFB1. | ||
| کلیدواژه‌ها [English] | ||
| Biosensor, modeling, COMSOL, Artificial Neural Network, Genetic Algorithm | ||
| مراجع | ||
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